Tabular Data Foundations

Adapted from Steve Bagley and based on R for Data Science by Hadley Wickham

Edited by David Connell

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You will get more out of this tutorial if you try out these things in R yourself!!

> 1 +2
> 1+ 2
> 1+2
> 1 + 2

These all do the same thing. The result of each line is 3:

[1] 3

> 1 + 2 * 3 # R respects order of operations
[1] 7
> 3/4
[1] 0.75
> 6^3
[1] 216
> log(10) # natural log
[1] 2.302585
> log10(10) # log base 10
[1] 1
> sqrt(16)
[1] 4

> c(2.1, -4, 22)
[1]  2.1 -4.0 22.0

• A vector is a one-dimensional sequence of zero or more numbers (or other values).
• Vectors are created by wrapping the values separated by commas with the c( ) function, which is short for “combine”

> 1:50
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

• The colon : is a handy shortcut to create a vector that is a sequence of integers from the first number to the second number (inclusive).
• Many R functions and operators automatically work when you input with multi-element vectors.
• Long vectors wrap around. (Your screen may have a different width than what is shown here.)
• Look at the [ ] notation. The second output line starts with 26, which is the 26^th element of the vector.
• This notation will help you figure out where you are in a long vector.

• An operation is elementwise (or element-wise) if the action you perform on a vector produces a vector with the same dimensions as the original.

• The code below multiplies each element of 1:10 by the corresponding element of 1:10, that is, it squares each element.

> (1:10)*(1:10)
 [1]   1   4   9  16  25  36  49  64  81 100

• Equivalently, we could use exponentiation:

> (1:10)^2
 [1]   1   4   9  16  25  36  49  64  81 100

• R does not evaluate strictly left to right.
• R respects the traditional mathematical order of operations (PEMDAS), but R has special operators that have their own precedence, a kind of priority for evaluation.
• For example, the sequence operator : has a higher precedence than addition +.

> 1 + 0:10
 [1]  1  2  3  4  5  6  7  8  9 10 11
> 0:10 + 1 # which operator gets executed first?
 [1]  1  2  3  4  5  6  7  8  9 10 11
> (0:10) + 1
 [1]  1  2  3  4  5  6  7  8  9 10 11
> 0:(10 + 1)
 [1]  0  1  2  3  4  5  6  7  8  9 10 11

• ADVANCED: check out a complete list of R operators and their precedence.

> x
[1] 10
> x / 5
[1] 2

• When R sees the name of a variable, it uses the stored value of that variable in the calculation.
• Here R uses the value of x, which is 10.
• / is the division operator.
• We can break complex calculations into named parts. This is a simple, but very useful kind of abstraction.

> x <- 10
> x
[1] 10
> x <- x + 1
> x
[1] 11

• If you assign to a name with an existing value, that value is overwritten.
• There is no way to undo an assignment, so be careful in reusing variable names.

• It is important to pick meaningful variable names.
• Names can be too short, so don't use x and y everywhere.
• Names can be too long (Main.database.first.object.header.length).
• Avoid silly names.
• Pick names that will make sense to someone else (including the person you will be in six months).
• ADVANCED: See ?make.names for the complete rules on what can be a name.

> a <- 1
> A # this causes an error because A does not have a value

Error: object 'A' not found

• R cares about upper and lower case in names.
• We also see that some error messages in R are a bit obscure.

• Make a variable that represents the age you will be at the end of this year
• Make a variable that represents the current year
• Use them to compute the year of your birth and save that as a variable
• Print the value of that variable

> my_age_end_of_year = 31
> this_year = 2022
> my_birth_year = this_year - my_age_end_of_year
> my_birth_year
[1] 1991

• Functions are the backbone of the R programming language; the majority of things you will do in R rely on them!
• A function is a set of statements that performs a specific task
• User is able to call or pass information to the function
• Function will perform task and return object or value or action

Where do functions come from?

• Many built into base R
• Many many more available through different package libraries
• ADVANCED: You can create your own!

Source: OOMPH course PHW251 - R for Public Health

• To call a function, type the function name, then the argument or arguments in parentheses. Arguments are the data and/or options we want the function to work on.
• Use a comma to separate the arguments, if more than one.

> sqrt(2)
[1] 1.414214

• Many basic R functions operate on multi-element vectors as easily as on vectors containing a single number.

> sqrt(0:10)
 [1] 0.000000 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751
 [9] 2.828427 3.000000 3.162278

> x <- 4
> sqrt(x)
[1] 2
> x
[1] 4
> y <- sqrt(x)
> y
[1] 2
> x <- 10
> y
[1] 2

• What do you observe about the value of y after changing the value of x?

• functions generally do not affect the variables you pass to them (x remains the same after sqrt(x))
• The results of a function call will simply be printed out if you do not save the result to a variable
• Saving the result to a variable lets you use it later, like any other variable you define manually
• Once a variable has been assigned (y), it keeps its value until updated, even if you change other variables (x) that went into the original assignment of that variable

> sum

• start typing sum, then hit the TAB key (or just wait a second)
• RStudio will display help on all functions that start sum.
• Use (up arrow, down arrow) to move through the list.
• Use RETURN or ENTER to select the current item.

Type ?name for help on name. Example:

> ?log

• This will show information about the log function (and related functions) in the Help pane, including the name and meaning of the arguments and returned values.
• The help display is hyperlinked, so clicking on a blue link will take you to related material.
• R documentation often describes a set of functions, such as all the ones related to logarithms, on a single help page.
• Parts of the R documentation are rather obscure.

> weights <- c(1.1, 2.2, 3.3)

• These weights are in pounds.
• Convert them to kilograms.
• (Hint: 2.2 lb = 1.0 kg)

> weights <- c(1.1, 2.2, 3.3)
> # this divides the weights, element-wise, by the conversion factor:
> weights / 2.2
[1] 0.5 1.0 1.5

> shoesize <- c(9, 12, 6, 10, 10, 16, 8, 4)
> shoesize
[1]  9 12  6 10 10 16  8  4
> sum(shoesize)
[1] 75
> sum(shoesize)/length(shoesize)
[1] 9.375
> mean(shoesize)
[1] 9.375

> x <- c(7, 3, 1, 9)

• Subtract the mean of x from x, and then sum the result.

> x <- c(7, 3, 1, 9)
> mean(x)
[1] 5
> x - mean(x)
[1]  2 -2 -4  4
> sum(x - mean(x)) # answer in one expression
[1] 0

• Writing a series of expressions in the console rapidly gets messy and confusing.
• The console window gets reset when you restart RStudio.
• It is better (and easier) to write expressions and functions in the script pane (upper left), building up your analysis.
• There, you can enter expressions, evaluate them, and save the contents to a .R file for later use.

> # This is a comment
> 1 + 2 # add some numbers
[1] 3

• Use a # to start a comment.
• A comment extends to the end of the line and is ignored by R.

If you're working in R locally (installed on your computer), you will need to install the tidyverse package. If you're on DataHub it has already been installed.

• Do this:

> install.packages("tidyverse")

> library("tidyverse")
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✓ tibble  3.1.5     ✓ dplyr   1.0.7
✓ tidyr   1.1.4     ✓ stringr 1.4.0
✓ readr   2.0.2     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

• “tidyverse” is a coherent set of packages for operating a kind of data called the “data frame”.
• It is not built-in, so you need to install it (once), then load it each time you restart R.
• Put library("tidyverse") at the top of every script file.

A data frame is one of the most powerful features in R.

• This is a compound data structure that can contain different types of data, similar to an Excel spreadsheet.
• Typically, each row in a data frame contains information about one instance of some (real-world) object.
• Each column can be thought of as a variable, containing the values for the corresponding instances.
• All the values in one column should be of the same type (a number, a category, text, etc.), but different columns can be of different types.
• Each column is actually a vector, so a data frame is like a list of vectors

> mtc
# A tibble: 32 × 11
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
 2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
 3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
 4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
 5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
 6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
 7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
 8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
 9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
# … with 22 more rows

• This data set contains data on 32 different cars.
• A tibble is a kind of data frame. This one has 32 rows and 11 columns. We only see the first 10 rows because of limited slide/screen space.
• Across the top is the name of each column. The next row shows the type of data in the column. <dbl>, means double-precision floating point number, which is a computer science term for any number with a decimal point in it (e.g. 1.3333, 3.14159, 1.0)

> mtc
# A tibble: 32 × 11
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
 2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
 3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
 4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
 5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
 6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
 7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
 8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
 9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
# … with 22 more rows

• Typically, each row in a data frame describes an instance of some (real-world) object. (Yes: one row for each model of car.)
• Each column contains the values of a variable for the corresponding instance. (Yes: one column for each variable.)

> mtc = read_csv("https://tinyurl.com/mtcars-csv")

• read_csv (from the readr package, part of tidyverse) reads in data frames that are stored in .csv files (.csv = comma-separated values)
• these files may be online (accessed via URL), or local, in which case the syntax is read_csv("path/to/file/mtcars.csv")
• try ?read_csv to learn a bit more
• .csvs can also be exported from spreadsheets and databases and then saved locally to be read into R.

• you can use tibble() to make your own data frames from scratch in R
• you don't need to do this yourself–we're just going to use this data in some examples

> my_data = tibble( # newlines don't do anything, just increase code readability
+   mrn = c(1, 2, 3, 4),
+   age = c(33, 48, 8, 29)
+ )
> my_data
# A tibble: 4 × 2
    mrn   age
  <dbl> <dbl>
1     1    33
2     2    48
3     3     8
4     4    29

• dim() gives the dimensions of the data frame. ncol() and nrow() give you the number of columns and the number of rows, respectively.

> dim(my_data)
[1] 4 2
> ncol(my_data)
[1] 2
> nrow(my_data)
[1] 4

• names() gives you the names of the columns (a vector)

> names(my_data)
[1] "mrn" "age"

• glimpse() shows you a lot of information

> glimpse(my_data)
Rows: 4
Columns: 2
$ mrn <dbl> 1, 2, 3, 4
$ age <dbl> 33, 48, 8, 29

• head() returns the first n rows

> head(my_data, n=2)
# A tibble: 2 × 2
    mrn   age
  <dbl> <dbl>
1     1    33
2     2    48

The rest of this section shows the basic data frame functions (“verbs”) in the dplyr package (part of tidyverse). Each operation takes a data frame and produces a new data frame.

• filter() picks out rows according to specified conditions
• select() picks out columns according to their names
• arrange() sorts the row by values in some column(s)
• mutate() creates new columns, often based on operations on other columns

All verbs work similarly:

1. The first argument (arguments are the things inside the parentheses next to the function) is a data frame.
2. The subsequent arguments describe what to do with the data frame, using the variable names (without quotes).
3. The result is a new data frame.

Together these properties make it easy to chain together multiple simple steps to achieve a complex result. Let’s dive in and see the basics of how these verbs work.

> filter(mtc, mpg >= 25)
# A tibble: 6 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
2  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
3  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
4  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
5  26       4 120.     91  4.43  2.14  16.7     0     1     5     2
6  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2

• This produces (and prints out) a new tibble (data frame), which contains all the rows where the mpg value in that row is greater than or equal to 25.
• There are only 6 rows in this data frame.
• There are still 11 columns.

> filter(mtc, mpg >= 25, qsec < 19)
# A tibble: 4 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
2  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
3  26       4 120.     91  4.43  2.14  16.7     0     1     5     2
4  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2

• This filters by the conjunction of the two constraints—both must be satisfied.
• Constraints appear as second (and third…) arguments, separated by commas.

> filter(mtc, mpg > 60)
# A tibble: 0 × 11
# … with 11 variables: mpg <dbl>, cyl <dbl>, disp <dbl>, hp <dbl>, drat <dbl>,
#   wt <dbl>, qsec <dbl>, vs <dbl>, am <dbl>, gear <dbl>, carb <dbl>

• If the constraint is too severe, then you will select no rows, and produce a zero row sized tibble.

• == tests for equality (do not use = which is for assignment)
• > and < test for greater-than and less-than
• >= and <= are greater-than-or-equal and less-than-or-equal
• these can also be used directly on vectors outside of data frames

> c(1,5,-22,4) > 0
[1]  TRUE  TRUE FALSE  TRUE

• How many cars have engines with horsepower (hp) greater than 200?

> filter(mtc, hp > 200)
# A tibble: 7 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  14.3     8   360   245  3.21  3.57  15.8     0     0     3     4
2  10.4     8   472   205  2.93  5.25  18.0     0     0     3     4
3  10.4     8   460   215  3     5.42  17.8     0     0     3     4
4  14.7     8   440   230  3.23  5.34  17.4     0     0     3     4
5  13.3     8   350   245  3.73  3.84  15.4     0     0     3     4
6  15.8     8   351   264  4.22  3.17  14.5     0     1     5     4
7  15       8   301   335  3.54  3.57  14.6     0     1     5     8

• Answer: 7

> select(mtc, mpg, qsec, wt)
# A tibble: 32 × 3
     mpg  qsec    wt
   <dbl> <dbl> <dbl>
 1  21    16.5  2.62
 2  21    17.0  2.88
 3  22.8  18.6  2.32
 4  21.4  19.4  3.22
 5  18.7  17.0  3.44
 6  18.1  20.2  3.46
 7  14.3  15.8  3.57
 8  24.4  20    3.19
 9  22.8  22.9  3.15
10  19.2  18.3  3.44
# … with 22 more rows

• The select function will return a subset of the data frame, using only the requested columns in the order specified.

• select() can also be used to select everything except for certain columns by using the minus character -

> select(mtc, -hp)
# A tibble: 32 × 10
     mpg   cyl  disp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  21       6  160   3.9   2.62  16.5     0     1     4     4
 2  21       6  160   3.9   2.88  17.0     0     1     4     4
 3  22.8     4  108   3.85  2.32  18.6     1     1     4     1
 4  21.4     6  258   3.08  3.22  19.4     1     0     3     1
 5  18.7     8  360   3.15  3.44  17.0     0     0     3     2
 6  18.1     6  225   2.76  3.46  20.2     1     0     3     1
 7  14.3     8  360   3.21  3.57  15.8     0     0     3     4
 8  24.4     4  147.  3.69  3.19  20       1     0     4     2
 9  22.8     4  141.  3.92  3.15  22.9     1     0     4     2
10  19.2     6  168.  3.92  3.44  18.3     1     0     4     4
# … with 22 more rows

• select() has a friend called pull() which returns a vector instead of a (one-column) data frame

> select(mtc, hp)
# A tibble: 32 × 1
      hp
   <dbl>
 1   110
 2   110
 3    93
 4   110
 5   175
 6   105
 7   245
 8    62
 9    95
10   123
# … with 22 more rows
> pull(mtc, hp)
 [1] 110 110  93 110 175 105 245  62  95 123 123 180 180 180 205 215 230  66  52
[20]  65  97 150 150 245 175  66  91 113 264 175 335 109

> filter(mtc, mpg < 11)
# A tibble: 2 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  10.4     8   472   205  2.93  5.25  18.0     0     0     3     4
2  10.4     8   460   215  3     5.42  17.8     0     0     3     4
> head(mtc)
# A tibble: 6 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  21       6   160   110  3.9   2.62  16.5     0     1     4     4
2  21       6   160   110  3.9   2.88  17.0     0     1     4     4
3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1
4  21.4     6   258   110  3.08  3.22  19.4     1     0     3     1
5  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2
6  18.1     6   225   105  2.76  3.46  20.2     1     0     3     1

• select() and filter() are functions, so they do not modify their input. You can see mtc is unchanged after calling filter() on it. This holds for functions in general.

• To save a new version of mtc, assign it to a variable using <-

> mtc_first_row <- filter(mtc, mpg < 11)
> mtc_first_row
# A tibble: 2 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  10.4     8   472   205  2.93  5.25  18.0     0     0     3     4
2  10.4     8   460   215  3     5.42  17.8     0     0     3     4

• arrange takes a data frame and a column, and sorts the rows by the values in that column (ascending order).

> powerful <- filter(mtc, hp > 200)
> arrange(powerful, mpg)
# A tibble: 7 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  10.4     8   472   205  2.93  5.25  18.0     0     0     3     4
2  10.4     8   460   215  3     5.42  17.8     0     0     3     4
3  13.3     8   350   245  3.73  3.84  15.4     0     0     3     4
4  14.3     8   360   245  3.21  3.57  15.8     0     0     3     4
5  14.7     8   440   230  3.23  5.34  17.4     0     0     3     4
6  15       8   301   335  3.54  3.57  14.6     0     1     5     8
7  15.8     8   351   264  4.22  3.17  14.5     0     1     5     4

• This is useful if there is a tie in sorting by the first column.

> arrange(powerful, gear, disp)
# A tibble: 7 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  13.3     8   350   245  3.73  3.84  15.4     0     0     3     4
2  14.3     8   360   245  3.21  3.57  15.8     0     0     3     4
3  14.7     8   440   230  3.23  5.34  17.4     0     0     3     4
4  10.4     8   460   215  3     5.42  17.8     0     0     3     4
5  10.4     8   472   205  2.93  5.25  18.0     0     0     3     4
6  15       8   301   335  3.54  3.57  14.6     0     1     5     8
7  15.8     8   351   264  4.22  3.17  14.5     0     1     5     4

> arrange(powerful, desc(mpg))
# A tibble: 7 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  15.8     8   351   264  4.22  3.17  14.5     0     1     5     4
2  15       8   301   335  3.54  3.57  14.6     0     1     5     8
3  14.7     8   440   230  3.23  5.34  17.4     0     0     3     4
4  14.3     8   360   245  3.21  3.57  15.8     0     0     3     4
5  13.3     8   350   245  3.73  3.84  15.4     0     0     3     4
6  10.4     8   472   205  2.93  5.25  18.0     0     0     3     4
7  10.4     8   460   215  3     5.42  17.8     0     0     3     4

> mtc_vars_subset = select(mtc, mpg, hp)
> mutate(mtc_vars_subset, gpm = 1/mpg)
# A tibble: 32 × 3
     mpg    hp    gpm
   <dbl> <dbl>  <dbl>
 1  21     110 0.0476
 2  21     110 0.0476
 3  22.8    93 0.0439
 4  21.4   110 0.0467
 5  18.7   175 0.0535
 6  18.1   105 0.0552
 7  14.3   245 0.0699
 8  24.4    62 0.0410
 9  22.8    95 0.0439
10  19.2   123 0.0521
# … with 22 more rows

• This uses mutate to add a new column to which is the reciprocal of mpg.
• The thing on the left of the = is a new name that you make up which you would like the new column to be called
• The expression on the right of the = defines what will go into the new column -mutate() can create multiple columns at the same time and use multiple columns to define a single new one

> mutate(mtc_vars_subset, # the newlines make it more readable
+       gpm = 1/mpg,
+       mpg_hp_ratio = mpg/hp) 
# A tibble: 32 × 4
     mpg    hp    gpm mpg_hp_ratio
   <dbl> <dbl>  <dbl>        <dbl>
 1  21     110 0.0476       0.191 
 2  21     110 0.0476       0.191 
 3  22.8    93 0.0439       0.245 
 4  21.4   110 0.0467       0.195 
 5  18.7   175 0.0535       0.107 
 6  18.1   105 0.0552       0.172 
 7  14.3   245 0.0699       0.0584
 8  24.4    62 0.0410       0.394 
 9  22.8    95 0.0439       0.24  
10  19.2   123 0.0521       0.156 
# … with 22 more rows

• As before, the result of this function is only saved if you assign it to a variable. In this example, mtc_vars_subset is unchanged after the mutate.

• filter() picks out rows according to specified conditions
• select() picks out columns according to their names
• arrange() sorts the row by values in some column(s)
• mutate() creates new columns, often based on operations on other columns

All verbs work similarly:

1. The first argument (arguments are the things inside the parentheses next to the function) is a data frame.
2. The subsequent arguments describe what to do with the data frame, using the variable names (without quotes).
3. The result is a new data frame.

Together these properties make it easy to chain together multiple simple steps to achieve a complex result.

Download the full cheatsheet here